Learning Sciences & Emergent Technologies Hub

Connecting Advances in Emergent Technologies with the Learning Sciences

The Learning Sciences and Emergent Technologies (LSET) Hub is a cross-university collaboration that organizes people and resources to develop, test, refine, and scale learning-sciences-infused uses of emergent technologies in teaching and scholarship. The Hub curates evidence-based guidance and tools, connects faculty and students, and convenes a community of practice that shares strategies, prototypes, and results. View the people of the LSET Hub.
 

The LSET/Pitt AI Readiness Framework

The LSET/Pitt AI Readiness Framework serves as a shared point of reference that can be adapted and used to guide thoughtful, context-specific discussions about how to assess current capabilities, identify gaps, and set priorities for advancing AI readiness.

Click here for the 2025-2026 GenAI Competency Framework
 

Co-Designing Teaching for a Gen AI World, Together

AI talk crowd

On April 22, more than 125 participants from local high schools, nonprofit organizations and the University of Pittsburgh came together to solve shared problems of practice around the use of generative AI tools among high school and college students "Co-Designing Teaching for a Gen AI World, Together." The event featured an overview of relevant learning sciences principles (see below "At a Glance: Five Learning Sciences Principles for Instructional Design") as well as a curated list of key problems of practice (see below "At a Glance: Five Problems of Practice.")  Read the Executive Summary Here

At a Glance: Five Learning Sciences Principles for Instructional Design

Cognitive Offloading
Productive Struggle
Metacognition
Expectancy-Value

Motivation shaped by belief, worth, and trade-offs

Students are motivated to engage when they believe they can succeed (expectancy) AND believe the task is worth their effort (value). Perceived cost - time, effort, lost opportunities - can override both.

Bridge to Practice Expectancy-Value   Expectancy-Value infographic

Collaborative Reasoning

At a Glance: Five Problems of Practice

The following is a curated list of key problems of practice (PoP), which the LSET team identified based on interviews with K-12 and higher education faculty, surveys of Pitt faculty and students, and an extensive literature review.

  • Student Awareness of Gen AI Impacts
    How might we design instructional experiences that build students' awareness of when and how GenAI use is helping or harming their learning?   PoP Student Awareness
  • Assessment of Learning Process
    How might we design assessment approaches to focus on students' process of learning related to the developmental goal?   PoP Assessment
  • Intentional GenAI Use/Non-Use in Writing
    How might we design use/non-use of AI within writing tasks to intentionally develop targeted cognitive skills?   PoP Writing
  • Critical Engagement with GenAI Output
    How do we help learners engage in systematic validation processes of Gen AI output?   PoP Validation
  • Shared Understanding of Course Assignments
    How might we increase the shared understanding of the purpose and meaning of courses and course assignments?   PoP Shared Understanding

Resources: LSET

Weekly AI in Higher Education Report

LSET member Alan Lesgold uses Manus to produce a fully LLM-generated weekly report summarizing key articles related to AI in HigherEd. The goal is to provide a sense of the issues and developments in policy and practice. These Manus-generated reports are not reviewed by humans and should be read with recognition that there could be inaccuracies. Each summarized article includes a link to the source and we recommend reviewing the original article before using or acting on the summarized content.

Courses

LSET shares a compilation of courses related to AI and other emergent technologies available at the University of Pittsburgh.

  • HAIL has gathered data on the people, centers, and courses connected with AI. This is a link to HAIL’s Rol-AI-Dex which can be used to search for Pitt AI-related courses.
  • This spreadsheet is provided by HAIL and lists the known Pitt AI-related courses available at the University.
  • INFSCI 1499 Special Topics - AI Literacy: Foundations for Critical Thinking and Informed Use
    Faculty: LRDC Research Scientist Angela Stewart
    This course introduces students from all backgrounds to the essential concepts of artificial intelligence (AI) and its growing role in society - no technical experience required. Students will learn how to recognize AI in everyday life, understand what it can and cannot do, and critically examine its impacts across contexts such as education, government, or media. Through discussions, interactive activities, case studies, and hands-on engagement with popular AI tools, students will develop the skills to evaluate when and how AI should be used. They will also learn to communicate its limitations and possibilities to general audiences and apply it thoughtfully in their personal, educational, and career pursuits. By the end of this course, students will be equipped with the knowledge and confidence to navigate a world increasingly shaped by artificial intelligence and make informed decisions about how these technologies influence their lives and futures.
  • AI PowerUP Class
    Registration information coming soon

White Papers

LSET does not, as a collective, produce white papers but individual LSET members often write blog posts, white papers, position papers, and the like. This section shares these individually produced artifacts.

Publications

LSET does not, as a collective, produce publications but individual LSET members’ relevant publications are shared here.

  • Coelho, R., & McCollum, A. (2025). Illuminating socially distributed identity resources in student writing through artificial intelligence to support the design of culturally informed learning experiences. Discourse Processes.
  • Coelho, R., Cheng, J., Pea, R., Schunn, C., & Liu, J. (2025). Advancing quantum information science pre-college education: The case for learning sciences collaboration. e-print.
  • Coelho, R., Bjune, A., E., Ellingsen, S., Solheim, B., M., Thormodsæter, R., Wasson, B., & Cotner, S. (2025). A call for clarity: Biology students advocate for guidelines for the use of generative AI in higher education. (2025). Journal of Science Education and Technology.

  • Savelka, J., Ashley, K.D., Gray, M.A., Westermann, H., & Xu, H. (2023). Explaining Legal Concepts with Augmented Large Language Models (GPT-4). Proceedings of the International Conference on Artifical Intelligence and Law (ICAIL 2023), University of Minho Law School, Braga, Portugal.

     

Resources: Pitt

 

What's News

September 16, 2025

Pitt Hosts its First Quantum Summer Camp for Local High Schoolers

July 16, 2025

University Students Feel Anxious, Confused, and Distrustful about AI in the Classroom and among Their Peers

May 12, 2025

Marc Coutanche authored new blog for Psychology Today: How to Use AI Without Forgetting Everything You Learned.

February 6, 2025

AI and How We Teach Writing

Partners

LSET is committed to building a community at Pitt to foster new research opportunities.

The Learning Sciences & Emergent Technologies (LSET) Hub is a cross-university collaboration-anchored by the Learning Research & Development Center, the School of Computing & Information, the University Center for Teaching & Learning, the Dietrich School of Arts & Sciences, and the Health Sciences Information Technology. Our aim is to help Pitt lead nationally in the ethical, evidence-based integration of digital technologies, advancing student success and faculty excellence while aligning with the Digital Future goals in the Plan for Pitt 2028.

Broadening Engagement in STEM Center (BE-STEM)

Institute for Learning

University Center for Teaching and Learning

School of Computing and Information

Learning Technology at LRDC

Health Sciences IT Education Technology

Dietrich School of Arts & Sciences Department of English